Systems and methods for motion correction in medical imaging

ABSTRACT

Systems and methods for motion correction in medical imaging are provided in the present disclosure. The systems may obtain at least two image sequences relating to a subject. Each of the at least two image sequences may be reconstructed based on image data that is acquired by a medical imaging device during one of at least two time periods. The subject may undergo a physiological motion during the at least two time periods. The systems may generate, based on the at least two image sequences, at least one corrected image sequence relating to the subject by correcting, using a motion correction model, an artifact caused by the physiological motion.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority of Chinese Patent Application No.201911146934.5, filed on Nov. 21, 2019, the contents of which are herebyincorporated by reference.

TECHNICAL FIELD

The disclosure generally relates to the field of medical imageprocessing, and more particularly relates to systems and methods formotion correction in medical imaging.

BACKGROUND

In medical imaging or image processing, some factors may cause anartifact in an image of an object, which may reduce the image quality ofthe image. In some embodiments, the artifact may include a motionartifact. The motion artifact may be caused by a motion (e.g., aphysical motion or a physiological motion) of the object during theimaging process. Taking the heart of a subject as an example, an imageof the heart may include a motion artifact caused by a motion of theheart. A correction algorithm may be used for correcting a motionartifact in an image.

SUMMARY

In an aspect of the present disclosure, a system for motion correctionin medical imaging are provided in the present disclosure. The systemmay include at least one storage device including a set of instructionsand at least one processor configured to communicate with the at leastone storage device. When executing the set of instructions, the at leastone processor may be configured to direct the system to perform thefollowing operations. The at least one processor may obtain at least twoimage sequences relating to a subject. Each of the at least two imagesequences may be reconstructed based on image data that is acquired by amedical imaging device during one of at least two time periods. Thesubject may undergo a physiological motion during the at least two timeperiods. The at least one processor may generate, based on the at leasttwo image sequences, at least one corrected image sequence relating tothe subject by correcting, using a motion correction model, an artifactcaused by the physiological motion.

In some embodiments, each of at least two image sequences may relate tothe heart of the subject, and the physiological motion may include acardiac motion.

In some embodiments, the physiological motion may include a motioncycle.

In some embodiments, a duration of each of the at least two time periodsmay be shorter than a duration of the motion cycle.

In some embodiments, the at least two image sequences may be acquiredwithin a same motion cycle or different motion cycles.

In some embodiments, the obtaining at least two image sequences relatingto a subject may include obtaining a plurality of image sequencesrelating to the subject, and determining the at least two imagesequences from the plurality of image sequences, wherein the at leasttwo image sequences satisfy a condition relating to a motion amplitude.

In some embodiments, the generating, based on the at least two imagesequences, at least one corrected image sequence relating to the subjectusing a motion correction model may include determining at least tworanked image sequences by ranking, based on the at least two timeperiods, the at least two image sequences; inputting, according to theranking of the at least two image sequences, the at least two rankedimage sequences into the motion correction model; and outputting the atleast one corrected image sequence by the motion correction model.

In some embodiments, 7, the at least two image sequences may be acquiredwithin a same motion cycle. The determining at least two ranked imagesequences by ranking, based on the at least two time periods, the atleast two sample image sequences may include ranking, according to achronological order of the at least two time periods, the at least twosample image sequences.

In some embodiments, the at least two image sequences may be acquiredwithin different motion cycles. The determining at least two rankedimage sequences by ranking, based on the at least two sample timeperiods, the at least two sample image sequences may include for each ofthe at least two time periods, determining a relative position of thetime period with respect to its corresponding motion cycle; ranking,according to an order of the at least two relative positions, the atleast two time periods; and ranking, according to the at least tworanked time periods, the at least two sample image sequences.

In some embodiments, the motion correction model may be obtainedaccording to operations including obtaining a plurality of samples eachof which may include at least two sample image sequences relating to asample subject and at least one gold standard image sequencecorresponding to the at least two sample image sequences and generatingthe motion correction model by training a machine learning model usingthe plurality of samples. Each of the at least two image sequences maybe reconstructed based on image data acquired by the medical imagingdevice during one of at least two sample time periods. The at least onegold standard image sequence may have no motion artifact.

In another aspect of the present disclosure, a method for motioncorrection in medical imaging is provided. The method may be implementedon a computing device including at least one processor and at least onestorage device. The method may include obtaining at least two imagesequences relating to a subject. Each of the at least two imagesequences may be reconstructed based on image data that is acquired by amedical imaging device during one of at least two time periods. Thesubject may undergo a physiological motion during the at least two timeperiods. The method may also include generating, based on the at leasttwo image sequences, at least one corrected image sequence relating tothe subject by correcting, using a motion correction model, an artifactcaused by the physiological motion.

In some embodiments, the generating, based on the at least two imagesequences, at least one corrected image sequence relating to the subjectusing a motion correction model may include determining at least tworanked image sequences by ranking, based on the at least two timeperiods, the at least two image sequences; inputting, according to theranking of the at least two image sequences, the at least two rankedimage sequences into the motion correction model; and outputting the atleast one corrected image sequence by the motion correction model.

In some embodiments, the at least two image sequences may be acquiredwithin a same motion cycle. The determining at least two ranked imagesequences by ranking, based on the at least two time periods, the atleast two sample image sequences may include ranking, according to achronological order of the at least two time periods, the at least twosample image sequences.

In some embodiments, the at least two image sequences may be acquiredwithin different motion cycles. The determining at least two rankedimage sequences by ranking, based on the at least two sample timeperiods, the at least two sample image sequences may include for each ofthe at least two time periods, determining a relative position of thetime period with respect to its corresponding motion cycle; ranking,according to an order of the at least two relative positions, the atleast two time periods; and ranking, according to the at least tworanked time periods, the at least two sample image sequences.

In some embodiments, the motion correction model may be obtainedaccording to operations including obtaining a plurality of samples eachof which may include at least two sample image sequences relating to asample subject and at least one gold standard image sequencecorresponding to the at least two sample image sequences and generatingthe motion correction model by training a machine learning model usingthe plurality of samples. Each of the at least two image sequences maybe reconstructed based on image data acquired by the medical imagingdevice during one of at least two sample time periods. The at least onegold standard image sequence may have no motion artifact.

In another aspect of the present disclosure, a system for generating amotion correction model is provided. The system may include at least onestorage device including a set of instructions and at least oneprocessor configured to communicate with the at least one storagedevice. When executing the set of instructions, the at least oneprocessor may be configured to direct the system to perform thefollowing operations. The at least one processor may obtain a pluralityof samples each of which may include at least two sample image sequencesrelating to a sample subject and at least one gold standard imagesequence corresponding to the at least two sample image sequences. Eachof the at least two image sequences may be reconstructed based on imagedata acquired by a medical imaging device during one of at least twosample time periods. The sample subject may undergo a physiologicalmotion during the at least two sample time periods. The at least onegold standard image sequence may have no artifact caused by thephysiological motion. The at least one processor may generate the motioncorrection model by training a machine learning model using theplurality of samples.

In some embodiments, each of at least two sample image sequences mayrelate to the heart of a sample subject, and the physiological motionmay include a cardiac motion.

In some embodiments, the physiological motion may include a motioncycle.

In some embodiments, a duration of each of the at least two sample timeperiods may be shorter than a duration of the motion cycle.

In some embodiments, the generating the motion correction model bytraining a machine learning model using the plurality of samples mayinclude for each of the plurality of samples, ranking, according to theat least two sample time periods, the at least two sample imagesequences, and generating the motion correction model by training themachine learning model using the at least two ranked image sequences andthe at least one gold standard image sequence corresponding to each ofthe plurality of samples.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device may be implemented accordingto some embodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device according to some embodimentsof the present disclosure;

FIG. 4A and FIG. 4B are block diagrams illustrating exemplary processingdevices according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for motioncorrection according to some embodiments of the present disclosure;

FIG. 6 is a schematic diagram illustrating an exemplary process ofdetermining at least two image sequences according to some embodimentsof the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for generating amotion correction model according to some embodiments of the presentdisclosure; and

FIG. 8 is a schematic diagram illustrating an example of motioncorrection according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, sections or assembly of differentlevels in ascending order. However, the terms may be displaced byanother expression if they achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 210 as illustrated in FIG. 2) may beprovided on a computer-readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedin connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items. The term “image” in the present disclosure isused to collectively refer to image data (e.g., scan data) and/or imagesof various forms, including a two-dimensional (2D) image, athree-dimensional (3D) image, a four-dimensional (4D) image, etc. Theterm “pixel” and “voxel” in the present disclosure are usedinterchangeably to refer to an element of an image.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The term “imaging modality” or “modality” as used herein broadly refersto an imaging method or technology that gathers, generates, processes,and/or analyzes imaging information of a subject. The subject mayinclude a biological subject (e.g., a human, an animal), anon-biological subject (e.g., a phantom), etc. In some embodiments, thesubject may include a specific part, organ, and/or tissue of thesubject. For example, the subject may include head, brain, neck, body,shoulder, arm, thorax, cardiac, stomach, blood vessel, soft tissue,knee, feet, or the like, or any combination thereof. The term “object”or “subject” are used interchangeably.

An aspect of the present disclosure relates to systems and methods formotion correction in medical imaging. The systems and methods may obtainat least two image sequences relating to a subject (e.g., the heart of apatient). Each of the at least two image sequences may be reconstructedbased on image data that is acquired by a medical imaging device duringone of at least two time periods. The subject may undergo aphysiological motion (e.g., a cardiac motion) during the at least twotime periods. The systems and the methods may generate, based on the atleast two image sequences, at least one corrected image sequencerelating to the subject by correcting, using a motion correction model(e.g., a trained deep learning model), an artifact caused by thephysiological motion.

According to some embodiments of the present disclosure, the systems andmethods may rank at least two image sequences according to an order ofthe at least two time periods. The systems and methods may input the atleast two ranked image sequences into the motion correction model andoutput the at least one corrected image sequence. During traditionalmotion correction algorithm, image data acquired during a scan of thesubject may need to be reconstructed; the reconstructed image sequencesmay be segmented and determine a motion trajectory of the subject; andthe motion trajectory of the subject may be used for motion correction,which is complex and time-consuming. In comparison with the traditionalmotion correction algorithm, the correction process using the motioncorrection model may reconstruct a portion of the image data acquiredduring the scan and directly output a corrected image sequence byinputting reconstructed image sequences into the motion correctionmodel, which can improve an efficiency of the motion correction andreduce a computing complexity of the motion correction. Moreover, themotion correction model may be trained based on data from a plurality ofsample objects, which can better account for differences in differentobjects and improve the accuracy of the motion correction.

FIG. 1 is a schematic diagram illustrating an exemplary imaging system100 according to some embodiments of the present disclosure. In someembodiments, the imaging system may be configured for non-invasivebiomedical imaging (e.g., cardiac imaging, lung imaging), such as fordisease diagnostic, treatment, and/or research purposes. In someembodiments, the imaging system may include a single modality imagingsystem and/or a multi-modality imaging system. The single modalityimaging system may include, for example, an X-ray imaging system, acomputed tomography (CT) system (e.g., a spiral CT system, a cone-beamCT system, etc.), a single photon emission computed tomography (SPECT)system, a digital radiography (DR) system, an ultrasonic imaging system,a positron emission tomography (PET) system, a magnetic resonanceimaging (MRI) system, or the like, or any combination thereof. Themulti-modality imaging system may include, for example, a PET-CT system,an X-ray-MRI system, a SPECT-MRI system, an image-guided radiotherapysystem (e.g., a CT guided radiotherapy system), etc. It should be notedthat the imaging system described below is merely provided forillustration purposes, and not intended to limit the scope of thepresent disclosure.

In some embodiments, the imaging system 100 may include modules and/orcomponents for performing imaging and/or related analysis. Merely by wayof example, as illustrated in FIG. 1, the imaging system 100 may includea medical imaging device 110, a processing device 120, a storage device130, one or more terminals 140, and a network 150. The components in theimaging system 100 may be connected in various ways. Merely by way ofexample, the medical imaging device 110 may be connected to theprocessing device 120 through the network 150 or directly as illustratedin FIG. 1. As another example, the terminal(s) 140 may be connected tothe processing device 120 via the network 150 or directly as illustratedin FIG. 1.

The medical imaging device 110 may be configured to acquire imaging datarelating to a subject. The imaging data relating to a subject mayinclude an image (e.g., an image slice), projection data, or acombination thereof. In some embodiments, the imaging data may betwo-dimensional (2D) imaging data, three-dimensional (3D) imaging data,four-dimensional (4D) imaging data, or the like, or any combinationthereof. In some embodiments, the medical imaging device 110 may includea CT device, an X-ray imaging device, a DR device, a SPECT device, anultrasonic imaging device, a PET device, an MRI device, a PET-CT device,an X-ray-MRI device, a SPECT-MRI device, an image-guided radiotherapydevice (e.g., a CT guided radiotherapy device), etc. The followingdescriptions are provided with reference to the medical imaging device110 being a CT device. It is understood that this is for illustrationpurposes and not intended to be limiting. In some embodiments, themedical imaging device 110 may include a radiation source, a detector, agantry, a table, etc. The radiation source and the detector may bemounted on the gantry. The subject may be placed on the table and movedto an imaging region of the imaging device. The radiation source mayinclude a tube configured to emit radioactive rays (e.g., X rays)traveling toward the subject. The detector may detect radiation (e.g.,X-rays) emitted from the imaging region of the medical imaging device110. In some embodiments, the detector may include one or more detectorunits. The detector unit(s) may include a scintillation detector (e.g.,a cesium iodide detector, a gadolinium oxysulfide detector), a gasdetector, etc. The detector unit(s) may include a single-row detectorand/or a multi-rows detector.

The processing device 120 may process data and/or information obtainedfrom the medical imaging device 110, the terminal(s) 140, and/or thestorage device 130. For example, the processing device 120 may generatea corrected image sequence by inputting at least two image sequencesaccording to a specific time order into a motion correction model. Asanother example, the processing device 120 may train the motioncorrection model using a plurality of samples. Each of the plurality ofsamples may include at least two sample image sequences relating to asample subject and at least one sample gold standard image sequencecorresponding to the at least two sample image sequences.

In some embodiments, the generation and/or updating of the motioncorrection model may be performed on a processing device, while theapplication of the motion correction model may be performed on adifferent processing device. In some embodiments, the generation and/orupdating of motion correction model may be performed on a processingdevice of a system different from the imaging system 100 or a serverdifferent from a server including the processing device 120 on which theapplication of motion correction model is performed. For instance, thegeneration and/or updating of motion correction model may be performedon a first system of a vendor who provides and/or maintains such amotion correction model and/or has access to training samples used togenerate motion correction model, while motion correction based on theprovided motion correction model may be performed on a second system ofa client of the vendor. In some embodiments, the generation and/orupdating of motion correction model may be performed on a processingdevice, while the application of motion correction model may beperformed on a different processing device. In some embodiments, thegeneration and/or updating of motion correction model may be performedonline in response to a request for motion correction. In someembodiments, the generation and/or updating of motion correction modelmay be performed offline.

In some embodiments, the motion correction model may be generated and/orupdated (or maintained) by, e.g., the manufacturer of the medicalimaging device 110 or a vendor. For instance, the manufacturer or thevendor may load the motion correction model into the imaging system 100or a portion thereof (e.g., the processing device 120) before or duringthe installation of the medical imaging device 110 and/or the processingdevice 120, and maintain or update the motion correction model from timeto time (periodically or not). The maintenance or update may be achievedby installing a program stored on a storage device (e.g., a compactdisc, a USB drive, etc.) or retrieved from an external source (e.g., aserver maintained by the manufacturer or vendor) via the network 150.The program may include a new model (e.g., a new motion correctionmodel) or a portion of a model that substitute or supplement acorresponding portion of the model.

In some embodiments, the processing device 120 may be a computer, a userconsole, a single server or a server group, etc. The server group may becentralized or distributed. In some embodiments, the processing device120 may be local or remote. For example, the processing device 120 mayaccess information and/or data stored in the medical imaging device 110,the terminal(s) 140, and/or the storage device 130 via the network 150.As another example, the processing device 120 may be directly connectedto the medical imaging device 110, the terminal(s) 140, and/or thestorage device 130 to access stored information and/or data. In someembodiments, the processing device 120 may be implemented on a cloudplatform. Merely by way of example, the cloud platform may include aprivate cloud, a public cloud, a hybrid cloud, a community cloud, adistributed cloud, an inter-cloud, a multi-cloud, or the like, or anycombination thereof.

The storage device 130 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 130 may store dataobtained from the medical imaging device 110, the terminal(s) 140 and/orthe processing device 120. For example, the storage device 130 may storeimage data acquired by the medical imaging device 110 during at leasttwo time periods. As another example, the storage device 130 may storeat least two image sequences relating to a subject. As still anotherexample, the storage device 130 may store a motion correction model formotion correction. As further another example, the storage device 130may store a plurality of samples for training the motion correctionmodel. In some embodiments, the storage device 130 may store data and/orinstructions that the processing device 120 may execute or use toperform exemplary methods/systems described in the present disclosure.In some embodiments, the storage device 130 may include a mass storagedevice, a removable storage device, a volatile read-and-write memory, aread-only memory (ROM), or the like, or any combination thereof.Exemplary mass storage devices may include a magnetic disk, an opticaldisk, a solid-state drive, etc. Exemplary removable storage devices mayinclude a flash drive, a floppy disk, an optical disk, a memory card, azip disk, a magnetic tape, etc. Exemplary volatile read-and-writememories may include a random access memory (RAM). Exemplary RAM mayinclude a dynamic RAM (DRAM), a double date rate synchronous dynamic RAM(DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. Exemplary ROM may include a mask ROM(MROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM),an electrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage device 130 may be implemented on a cloud platform. Merely byway of example, the cloud platform may include a private cloud, a publiccloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or any combination thereof.

In some embodiments, the storage device 130 may be connected to thenetwork 150 to communicate with one or more other components in theimaging system 100 (e.g., the processing device 120, the terminal(s)140, etc.). One or more components in the imaging system 100 may accessthe data or instructions stored in the storage device 130 via thenetwork 150. In some embodiments, the storage device 130 may be directlyconnected to or communicate with one or more other components in theimaging system 100 (e.g., the processing device 120, the terminal(s)140, etc.). In some embodiments, the storage device 130 may be part ofthe processing device 120.

In some embodiments, a user (e.g., a doctor, a technician, or anoperator) may interact with the imaging system 100 through the terminal(s) 140. For example, a corrected image sequence determined after motioncorrection may be displayed on an interface of the terminal 140. Theuser may perform a user operation to provide feedback on whether thecorrected image sequence satisfies an image quality. In someembodiments, the terminal(s) 140 may include a mobile device 140-1, atablet computer 140-2, a laptop computer 140-3, or the like, or anycombination thereof. In some embodiments, the mobile device 140-1 mayinclude a smart home device, a wearable device, a mobile device, avirtual reality device, an augmented reality device, or the like, or anycombination thereof. In some embodiments, the smart home device mayinclude a smart lighting device, a control device of an intelligentelectrical apparatus, a smart monitoring device, a smart television, asmart video camera, an interphone, or the like, or any combinationthereof. In some embodiments, the wearable device may include abracelet, a footgear, eyeglasses, a helmet, a watch, clothing, abackpack, a smart accessory, or the like, or any combination thereof. Insome embodiments, the mobile device may include a mobile phone, apersonal digital assistant (PDA), a gaming device, a navigation device,a point of sale (POS) device, a laptop, a tablet computer, a desktop, orthe like, or any combination thereof. In some embodiments, the virtualreality device and/or the augmented reality device may include a virtualreality helmet, virtual reality glasses, a virtual reality patch, anaugmented reality helmet, augmented reality glasses, an augmentedreality patch, or the like, or any combination thereof. For example, thevirtual reality device and/or the augmented reality device may include aGoogle Glass™, an Oculus Rift™, a Hololens™, a Gear VR™, etc. In someembodiments, the terminal(s) 140 may be part of the processing device120.

The network 150 may include any suitable network that can facilitate theexchange of information and/or data for the imaging system 100. In someembodiments, one or more components of the medical imaging device 110(e.g., a CT device), the terminal(s) 140, the processing device 120, thestorage device 130, etc., may communicate information and/or data withone or more other components of the imaging system 100 via the network150. For example, the processing device 120 may obtain at least twoimage sequences and/or a motion correction model from the storage device130 via the network 150. As another example, the processing device 120may obtain user instructions from the terminal(s) 140 via the network150. The network 150 may be and/or include a public network (e.g., theInternet), a private network (e.g., a local area network (LAN), a widearea network (WAN)), etc.), a wired network (e.g., an Ethernet network),a wireless network (e.g., an 802.11 network, a Wi-Fi network, etc.), acellular network (e.g., a Long Term Evolution (LTE) network), a framerelay network, a virtual private network (“VPN”), a satellite network, atelephone network, routers, hubs, switches, server computers, and/or anycombination thereof. Merely by way of example, the network 150 mayinclude a cable network, a wireline network, a fiber-optic network, atelecommunications network, an intranet, a wireless local area network(WLAN), a metropolitan area network (MAN), a public telephone switchednetwork (PSTN), a Bluetooth™ network, a ZigBee™ network, a near fieldcommunication (NFC) network, or the like, or any combination thereof. Insome embodiments, the network 150 may include one or more network accesspoints. For example, the network 150 may include wired and/or wirelessnetwork access points such as base stations and/or internet exchangepoints through which one or more components of the imaging system 100may be connected to the network 150 to exchange data and/or information.

In some embodiments, the imaging system 100 may further include aphysiological motion detection device (not shown) configured toacquire/detect a physiological motion of the subject. For example, thephysiological motion detection device may acquire information of thephysiological motion of the subject before, when, and/or after a scan isperformed on the subject by the medical imaging device 110. In someembodiments, the physiological motion detection device may include amedical monitor device (e.g., an electrocardiograph (ECG) monitor), or amobile device (e.g., a smart device, a wearable device, etc.) which maybe installed with an application to record the physiological motion ofthe subject. In some embodiments, the detection device may be operablyconnected to the network 150 to communicate with one or more componentsof the imaging system 100. One or more components of the imaging system100 may access data/information from the detection device via thenetwork 150. In some embodiments, the detection device may be directlyconnected to or communicate with one or more components of the imagingsystem 100 (e.g., the processing device 120, the terminal(s) 140, etc.).In some embodiments, the detection device may be external to the imagingsystem 100 but communicate with the imaging system 100 (e.g., via thenetwork 150).

It should be noted that the above description of the imaging system 100is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. For example, the imagingsystem 100 may include one or more additional components and/or one ormore components of the imaging system 100 described above may beomitted. Additionally or alternatively, two or more components of theimaging system 100 may be integrated into a single component. Acomponent of the imaging system 100 may be implemented on two or moresub-components.

FIG. 2 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device 200 may be implementedaccording to some embodiments of the present disclosure. The computingdevice 200 may be used to implement any component of the imaging systemas described herein. For example, the processing device 120 and/or aterminal 140 may be implemented on the computing device 200,respectively, via its hardware, software program, firmware, or acombination thereof. Although only one such computing device is shown,for convenience, the computer functions relating to the imaging system100 as described herein may be implemented in a distributed fashion on anumber of similar platforms, to distribute the processing load. Asillustrated in FIG. 2, the computing device 200 may include a processor210, storage 220, an input/output (I/O) 230, and a communication port240.

The processor 210 may execute computer instructions (program codes) andperform functions of the processing device 120 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, signals, datastructures, procedures, modules, and functions, which perform particularfunctions described herein. In some embodiments, the processor 210 mayperform instructions obtained from the terminal(s) 140. In someembodiments, the processor 210 may include one or more hardwareprocessors, such as a microcontroller, a microprocessor, a reducedinstruction set computer (RISC), an application-specific integratedcircuits (ASICs), an application-specific instruction-set processor(ASIP), a central processing unit (CPU), a graphics processing unit(GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field-programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors. Thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 200executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 200(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperations A and B).

The storage 220 may store data/information obtained from the medicalimaging device 110, the terminal(s) 140, the storage device 130, or anyother component of the imaging system 100. In some embodiments, thestorage 220 may include a mass storage device, a removable storagedevice, a volatile read-and-write memory, a read-only memory (ROM), orthe like, or any combination thereof. In some embodiments, the storage220 may store one or more programs and/or instructions to performexemplary methods described in the present disclosure.

The I/O 230 may input or output signals, data, and/or information. Insome embodiments, the I/O 230 may enable user interaction with theprocessing device 120. In some embodiments, the I/O 230 may include aninput device and an output device. Exemplary input devices may include akeyboard, a mouse, a touch screen, a microphone, or the like, or acombination thereof. Exemplary output devices may include a displaydevice, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Exemplary display devices may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), or the like, or a combination thereof.

The communication port 240 may be connected with a network (e.g., thenetwork 150) to facilitate data communications. The communication port240 may establish connections between the processing device 120 and themedical imaging device 110, the terminal(s) 140, or the storage device130. The connection may be a wired connection, a wireless connection, ora combination of both that enables data transmission and reception. Thewired connection may include an electrical cable, an optical cable, atelephone wire, or the like, or any combination thereof. The wirelessconnection may include a Bluetooth network, a Wi-Fi network, a WiMaxnetwork, a WLAN, a ZigBee network, a mobile network (e.g., 3G, 4G, 5G,etc.), or the like, or any combination thereof. In some embodiments, thecommunication port 240 may be a standardized communication port, such asRS232, RS485, etc. In some embodiments, the communication port 240 maybe a specially designed communication port. For example, thecommunication port 240 may be designed in accordance with the digitalimaging and communications in medicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device 300 according to someembodiments of the present disclosure. In some embodiments, one or morecomponents (e.g., a terminal 140 and/or the processing device 120) ofthe imaging system 100 may be implemented on the mobile device 300.

As illustrated in FIG. 3, the mobile device 300 may include acommunication platform 310, a display 320, a graphics processing unit(GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory360, and a storage 390. In some embodiments, any other suitablecomponent, including but not limited to a system bus or a controller(not shown), may also be included in the mobile device 300. In someembodiments, a mobile operating system 370 (e.g., iOS, Android, WindowsPhone, etc.) and one or more applications 380 may be loaded into thememory 360 from the storage 390 in order to be executed by the CPU 340.The applications 380 may include a browser or any other suitable mobileapps for receiving and rendering information relating to imageprocessing or other information from the processing device 120. Userinteractions with the information stream may be achieved via the I/O 350and provided to the processing device 120 and/or other components of theimaging system 100 via the network 150.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. The hardware elements, operating systems and programminglanguages of such computers are conventional in nature, and it ispresumed that those skilled in the art are adequately familiar therewithto adapt those technologies to generate an image as described herein. Acomputer with user interface elements may be used to implement apersonal computer (PC) or another type of work station or terminaldevice, although a computer may also act as a server if appropriatelyprogrammed. It is believed that those skilled in the art are familiarwith the structure, programming and general operation of such computerequipment and as a result, the drawings should be self-explanatory.

FIG. 4A and FIG. 4B are block diagrams illustrating exemplary processingdevices 120A and 120B according to some embodiments of the presentdisclosure. In some embodiments, the processing devices 120A and 120Bmay be embodiments of the processing device 120 as described inconnection with FIG. 1. In some embodiments, the processing devices 120Aand 120B may be respectively implemented on a processing unit (e.g., theprocessor 210 illustrated in FIG. 2 or the CPU 340 as illustrated inFIG. 3). Merely by way of example, the processing devices 120A may beimplemented on a CPU 340 of a terminal device, and the processing device120B may be implemented on a computing device 200. Alternatively, theprocessing devices 120A and 120B may be implemented on a same computingdevice 200 or a same CPU 340. For example, the processing devices 120Aand 120B may be implemented on a same computing device 200.

As shown in FIG. 4A, the processing device 120A may include an obtainingmodule 401 and a generation module 403.

The obtaining module 401 may be configured to obtain information/datafrom one or more components of the imaging device 100. For example, theobtaining module 401 may obtain a plurality of image sequences relatingto the subject. Each of the plurality of image sequences may correspondto one of a plurality of time periods. The obtaining module 401 mayobtain at least two image sequences relating to the subject from theplurality of image sequences. Each of the at least two sequences maycorrespond to one of least two time periods (also referred to as atleast two first time periods). The subject may undergo a physiologicalmotion (e.g., a cardiac motion) during the plurality of time periods. Asanother example, the obtaining module 401 may obtain physiologicalmotion data (e.g., an ECG image) relating to the subject that isdetected during the plurality of time periods. More descriptionsregarding the obtaining of the at least two sequences and thephysiological motion data may be found elsewhere in the presentdisclosure (e.g., operation 501 om FIG. 5 and the description thereof).

The generation model 403 may be configured to generate at least onecorrected image sequence relating to the subject based on the at leasttwo image sequences. For example, the generation module 403 may generatethe at least one corrected image sequence by using a motion correctionmodel. As used herein, a motion correction model refers to a machinelearning model (e.g., a deep learning model) configured for motionartifact correction based on at least two image sequences and timeinformation thereof. In some embodiments, the generation module 403 maydetermine at least two ranked image sequences by ranking, based on theat least two time periods, the at least two image sequences. Thegeneration module 403 may input the at least two ranked image sequences,according to the ranking of the at least two image sequences, into themotion correction model. The generation module 403 may output the atleast one image sequence by the motion correction model. Moredescriptions regarding the generation of the at least one correctedimage may be found elsewhere in the present disclosure (e.g., operation503 in FIG. 5 and the description thereof).

As shown in FIG. 4B, the processing device 120B may include an obtainingmodule 405 and a training module 407.

The obtaining module 405 may be configured to obtain data/informationthat can be used in training the motion correction model. For example,the obtaining module 405 may obtain a plurality of training samples.Each of the plurality of training samples may include at least twosample image sequences relating to a sample subject and at least onegold standard image sequence corresponding to the at least two sampleimage sequences. More descriptions regarding the obtaining of theplurality of training samples may be found elsewhere in the presentdisclosure (e.g., operation 701 in FIG. 7 and the description thereof).

The training module 407 may be configured to generate the motioncorrection model. For example, the training module 407 may generate themotion correction model by training a machine learning model using theplurality of samples. In some embodiments, the machine learning modelmay include a deep learning model. The deep learning model may include aneural network model, such as a U-NET model (e.g., a residual U-NETmodel, a dense U-NET model), a V-NET model, a super-resolutionconvolutional neural network (SRCNN) model, etc. In some embodiments,the training module 407 may divide the plurality of training samplesinto a first portion and a second portion (e.g., randomly). The firstportion may be used to train the machine learning model to obtain themotion correction model. The second portion may be used to test themotion correction model to determine whether the motion correction modelis satisfactory. In some embodiments, a ratio of a count (or number) ofthe first portion and a count (or number) of the second portion may be8:2, 9:1, 9.5:0.5, etc. More descriptions regarding the training of themotion correction model may be found elsewhere in the present disclosure(e.g., operation 703 in FIG. 7 and the description thereof).

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently, for persons having ordinary skills inthe art, multiple variations and modifications may be conducted underthe teachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.Each of the modules described above may be a hardware circuit that isdesigned to perform certain actions, e.g., according to a set ofinstructions stored in one or more storage media, and/or any combinationof the hardware circuit and the one or more storage media.

In some embodiments, the processing device 120A and/or the processingdevice 120B may be integrated and implemented on a same server and/orshare two or more of the modules. For instance, the processing devices120A and 120B may share a same obtaining module; that is, the obtainingmodule 401 and the obtaining module 405 are a same module. In someembodiments, any one of the modules in the processing device 120A or120B may be divided into two or more units. In some embodiments, theprocessing device 120A and/or the processing device 120B may include oneor more additional modules, such as a storage module (not shown) forstoring data.

FIG. 5 is a flowchart illustrating an exemplary process for motioncorrection according to some embodiments of the present disclosure. Insome embodiments, process 500 may be implemented as a set ofinstructions (e.g., an application) stored in a storage device (e.g.,the storage device 130, the storage 220, and/or the storage 390). Theprocessing device 120A (e.g., the processor 210, the CPU 340, and/or oneor more modules illustrated in FIG. 4A) may execute the set ofinstructions, and when executing the instructions, the processing device120A may be configured to perform the process 500. The operations of theillustrated process presented below are intended to be illustrative. Insome embodiments, the process 500 may be accomplished with one or moreadditional operations not described and/or without one or more of theoperations discussed. Additionally, the order of the operations ofprocess 500 illustrated in FIG. 5 and described below is not intended tobe limiting.

In some embodiments, when a medical imaging device (e.g., the medicalimaging device 110) performs a scan on a subject (e.g., the heart or thelung of a patient), the subject may undergo a physiological motion(e.g., a cardiac motion, a respiratory motion, etc.) during the scan,which may cause a motion artifact (e.g., a cardiac motion artifact, arespiratory motion artifact, etc.). Traditionally, to generate a motionartifact-free image sequence relating to the subject, among the imagedata acquired during the scan, only a portion thereof acquired when thesubject undergoes a minimal physiological motion is used for imagereconstruction. Alternatively, a plurality of image sequences may bereconstructed based on the image data acquired during the scan. Theplurality of image sequences may be segmented. The plurality ofsegmented image sequences may be registered to determine a motiontrajectory of the subject (e.g., the heart or the lung of the patient)during the scan. The motion trajectory may be used to correct the motionartifact. However, the aforementioned traditional technologies may betime-consuming and/or the application thereof may be limited in complexconditions of the physiological motion (e.g., a condition ofarrhythmia). In some embodiments, the process 500 may be performed formotion correction more efficiently and accurately. For illustrationpurposes, the process 500 is described with reference to a scanassociated with a heart (also referred to as a cardiac scan). It shouldbe noted that the process 500 is appliable to scanning of anotherportion of a subject during which the subject may undergo aphysiological motion thereby causing a motion artifact.

In 501, the processing device 120A (e.g., the obtaining module 401) mayobtain at least two image sequences relating to a subject (e.g., theheart of the patient). Each of the at least two image sequences maycorrespond to one of least two time periods (also referred to as atleast two first time periods). The subject may undergo a physiologicalmotion (e.g., a cardiac motion) during the at least two time periods.

In some embodiments, the medical imaging device 110 (e.g., a CT device)may perform a scan on the subject during a plurality of time periods toacquire a plurality of image data sets. Each of the plurality of imagedata sets may be acquired during one of the plurality of time periods.That is, during each of the plurality of time periods, the medicalimaging device 110 may rotate its radiation source by multiple gantryangles to acquire multiple image data sub-sets relating to the subject.Each of the multiple image data sub-sets may correspond to one of themultiple gantry angels. Each of the multiple image data sub-sets may bereconstructed to obtain an image such that the multiple image datasub-sets may be reconstructed to obtain multiple images. The multipleimages may constitute an image sequence (e.g., a 3D image includingmultiple image slices) corresponding to a time period, which can reflecta global condition of the subject during the time period. For example,during a time period, a CT device may rotate its radiation source by 240gantry angles to obtain 240 image data sub-sets relating to the heart ofthe patient. Accordingly, the time period may correspond to an imagesequence including 240 images relating to the heart.

During the scan of the subject during the plurality of time periods, thesubject may undergo the physiological motion. The physiological motionmay include a cardiac motion, a respiratory motion, or the like, or anycombination thereof. In some embodiments, the physiological motion mayinclude a motion cycle. Taking the cardiac motion as an example, thecardiac motion may include a cardiac motion cycle. The duration of thecardiac motion cycle may be 0.8 seconds (0.8 s), 1 s, etc. As usedherein, a duration of a time period (e.g., each of the plurality of timeperiods, each of the at least two time periods) may be shorter than theduration of the motion cycle. Merely by way of example, for the durationof the cardiac motion cycle being 1 s, a duration of a time period maybe 0.05 s, 0.1 s, 0.2 s, 0.3 s, etc. In some embodiments, as theduration of the time period is relatively short, an image sequencecorresponding to a time period may be represented by an image sequencecorresponding a time point within the time period (e.g., a start or endtime point of the time period, a middle time point of the time period,etc.) for brevity. In some embodiments, a start time point of the scan(or a start point of one of the plurality of time periods) maycorrespond to any position of a motion cycle. For example, the starttime point of one of the plurality of time periods (or scan cycles) maycorrespond to the start of a motion cycle. That is, the start time pointof one of the plurality of time periods may coincide with when a newmotion cycle starts. As another example, the start time point of one ofthe plurality of time periods may correspond to a time point within amotion cycle. That is, the start time point of one of the plurality oftime periods falls in the middle of a motion cycle (e.g., ¼ into amotion cycle, the mid-point of a motion cycle, ¾ into a motion cycle,etc.).

In some embodiments, the processing device 120A may obtain a pluralityof image sequences each corresponding to one of the plurality of timeperiods. Each of the plurality of image sequences may be reconstructedbased on an image data set acquired during one of the plurality of timeperiods. An image data set may include multiple image data sub-sets eachcorresponding to a gantry angle as described elsewhere in the presentdisclosure. The processing device 120A may determine the at least twoimage sequences from the plurality of image sequences. The at least twoimage sequences may include N image sequences. N may be an integergreater than 2 such as 2, 3, 5, 8, 10, 15, or the like. The at least twoimage sequences may be reconstructed based on image data acquired withina same motion cycle or different motion cycles. In some embodiments, theprocessing device 120A may determine image sequences that satisfy apreset condition as the at least two image sequences. For example, theprocessing device 120A may determine, from the plurality of imagesequences, image sequences that satisfies a preset image quality as theat least two image sequences. For instance, a motion artifact in each ofthe at least two image sequences may be less than a preset motionartifact threshold. As another example, the processing device 120A maydetermine, from the plurality of image sequences, any successive imagesequences as the at least two image sequences. That is, the at least twotime periods corresponding to the at least two image sequences may beany successive time periods within the plurality of time periods. Forinstance, the start time point of the scan may be denoted by 0 s forbrevity. For the duration of a time period being 0.05 s and N being 3,the at least two time periods may include 3 time periods denoted by 0s-0.05 s. 0.05 s-0.1 s, and 0.1 s-0.15 s.

As still another example, the processing device 120A may determine amotion amplitude of the subject for each of the plurality of imagesequences. The processing device 120A may determine the at least twoimage sequences based on the motion amplitude of each of the pluralityof image sequences. In some embodiments, the processing device 120A maydetermine a motion amplitude of the subject for a specific imagesequence based on adjacent image sequences of the specific imagesequences. For example, the processing device 120A may determine avariation of the subject between the specific image sequence and itspreceding and/or successive image sequences. As used herein, a precedingimage sequence of a specific image sequence refers to an image sequencereconstructed based on an image data set acquired in a time periodimmediately before the time period when the image data set on the basisof which the specific image sequence is reconstructed is acquired. Asused herein, a successive image sequence of a specific image sequencerefers to an image sequence reconstructed based on an image data setacquired in a time period immediately after the time period when theimage data set on the basis of which the specific image sequence isreconstructed is acquired. The processing device 120A may determine themotion amplitude of the subject for the specific image sequences basedon the variation.

In some embodiments, the processing device 120A may obtain physiologicalmotion data (e.g., an ECG image) relating to the subject that isdetected during the plurality of time periods. The physiological motiondata may reflect the physiological motion of the subject during theplurality of time periods. The processing device 120 may determine theat least two image sequences based on the physiological motion data. Forexample, for each of the plurality of time periods, the processingdevice 120A may determine a motion amplitude of the cardiac motion ofthe heart based on the ECG image relating to the heart. The processingdevice 120A may determine the at least two time periods from theplurality of time periods based on the plurality of motion amplitudes ofthe cardiac motion of the heart. For example, the processing device 120Amay rank the plurality of motion amplitudes. The processing device 120Amay determine the at least two time periods based on at least twominimum motion amplitudes in the plurality of ranked motion amplitudes.As another example, the processing device 120A may determine, from theplurality of motion amplitudes, at least two motion amplitudes each ofwhich is less than a threshold amplitude. The processing device 120A maydetermine the at least two time periods based on the at least two motionamplitudes. Further, the processing device 120 may obtain, from theplurality of image data sets, at least two image data sets based on theat least two time periods.

Each of the at least two image data sets may be acquired during one ofthe at least two time periods. The processing device 120 may determinethe at least two image sequences by reconstructing the at least twoimage data sets using a reconstruction algorithm. The reconstructionalgorithm may include an iterative reconstruction algorithm (e.g., astatistical reconstruction algorithm), a Fourier slice theoremalgorithm, a filtered back projection (FBP) algorithm, a fan-beamreconstruction algorithm, an analytic reconstruction algorithm, or thelike, or any combination thereof. Alternatively, the processing device120A may directly determine, from the plurality of image sequences, theat least two image sequences based on the at least two time periods.

In 503, the processing device 120A (e.g., the generation module 403) maygenerate, based on the at least two image sequences, at least onecorrected image sequence relating to the subject by correcting, using amotion correction model, an artifact caused by the physiological motion.

As used herein, a motion correction model refers to a machine learningmodel (e.g., a deep learning model) configured for motion artifactcorrection based on at least two image sequences and time informationthereof. In some embodiments, the motion correction model may be atrained deep learning model. Merely by way of example, the motioncorrection model may include a trained neural network model, such as atrained U-NET model (e.g., a trained residual U-NET model, a traineddense U-NET model), a trained V-NET model, a trained super-resolutionconvolutional neural network (SRCNN) model, etc.

In some embodiments, the processing device 120A (e.g., the obtainingmodule 401) may obtain the motion correction model from one or morecomponents of the imaging system 100 (e.g., the storage device 130, theterminals(s) 140) or an external source via a network (e.g., the network150). For example, the motion correction model may be previously trainedby a computing device (e.g., the processing device 120B), and stored ina storage device (e.g., the storage device 130, the storage 220, and/orthe storage 390) of the imaging system 100. The processing device 120Amay access the storage device and retrieve the motion correction model.In some embodiments, the motion correction model may be generated by acomputing device (e.g., the processing device 120B) by performing aprocess (e.g., process 700) for generating the motion correction modeldisclosed herein. More descriptions regarding the generation of themotion correction model may be found elsewhere in the present disclosure(e.g., FIG. 7 and relevant description thereof).

In some embodiments, the processing device 120A may determine at leasttwo ranked image sequences by ranking, based on the at least two timeperiods, the at least two image sequences. The processing device 120Amay input the at least two ranked image sequences, according to theranking of the at least two image sequences, into the motion correctionmodel. The processing device 120A may output the at least one imagesequence by the motion correction model.

In some embodiments, the processing device 120A may determine whetherthe at least two image sequences correspond to a same motion cycle. Inresponse to determining that the at least two image sequences correspondto the same motion cycle, the processing device 120A may directly rankthe at least two image sequences according to a chronological order ofat least two time periods (e.g., in descending or ascending order). Inresponse to determining that the at least two image sequences do notcorrespond to a same motion cycle (i.e., the at least two imagesequences corresponding to different motion cycles), for each of theplurality of time periods, the processing device 120 may determine arelative position of the time period with respect to its correspondingmotion cycle. The processing device 120A may rank the at least two timeperiods, according to an order (e.g., a descending or ascending order)of the at least two relative positions, based on the at least tworelative positions. The processing device 120A may determine the atleast two ranked image sequences by ranking, based on the at least tworanked time periods, the at least two image sequences.

Merely by way of example, for a cardiac scan during a plurality of timeperiods within 0 s-3 s, the duration of the cardiac cycle may be 1 s,and the duration of each of the plurality of time periods may be 0.2 s.The at least two image sequences may include 3 image sequencescorresponding to 3 time periods, e.g., 0 s-0.2 s, 1.4 s-1.6 s, and 2.2s-2.4 s. Assuming that the start point of the cardiac scan correspondsto a start position of a cardiac cycle, the cardiac scan may correspondto 15 motion cycles and the 3 image sequences may correspond to threedifferent motion cycles among the 15 motion cycles. For brevity, eachmotion cycle may be denoted by 0%-100% where 0% corresponds to a startposition of the motion cycle and 100% corresponds to an end position ofthe motion cycle. Accordingly, a relative position of the time period 0s-0.2 s with respect to its corresponding motion cycle may be denoted by0%-20%, a relative position of the time period 1.4 s-1.6 s with respectto its corresponding motion cycle may be denoted by 40%-60%, and arelative position of the time period 2.2 s-2.4 s with respect to itscorresponding motion cycle may be denoted by 20%-40%.

Alternatively, the relative position of a time period may be representedby the mid-point of the time period with respect to its motion cycle.For instance, the relative position of the time period 0 s-0.2 s withrespect to its corresponding motion cycle may be denoted by 10%, arelative position of the time period 1.4 s-1.6 s with respect to itscorresponding motion cycle may be denoted by 50%, and a relativeposition of the time period 2.2 s-2.4 s with respect to itscorresponding motion cycle may be denoted by 30%. The processing device120A may rank the 3 time periods according to an ascending order ordescending order of the 3 relative positions. For instance, the 3 rankedtime periods may be in a sequence of 0 s-0.2 s, 2.2 s-2.4 s, and 1.4s-1.6 s, or a sequence of 1.4 s-1.6 s, 2.2 s-2.4 s, and 0 s-0.2 s.Further, the processing device 120A may rank the 3 image sequences basedon the 3 ranked time periods. Further, the processing device 120A mayinput the 3 ranked image sequences to output at least one correctedimage sequence (e.g., a corrected image sequence).

According to some embodiments of the present disclosure, as aphysiological motion of a subject (e.g., a tissue or an organ) has acontinuity (e.g., a motion cycle), a corrected image sequence relatingto a subject may be determined using the motion correction model basedon at least two image sequences relating to the subject corresponding toleast two time periods. In such situations, the physiological motion ofthe subject during the at least two time periods may be taken intoconsideration by inputting the at least two image sequences in an order(e.g., a chronological order) into the motion correction model, whichcan facilitate the determination of a change of the subject due to thephysiological motion (e.g., a motion trajectory of the subject) duringthe at least two time periods, thereby facilitating the correction ofmotion artifact in a resultant image caused by the physiological motionaccording to the physiological motion and a relevant change of thesubject due to the physiological motion during the at least two timeperiods.

In some embodiments, a count (or number) of the at least one correctedimage sequence output by the motion correction model may be determineddepending on training samples used for training the motion correctionmodel. Each of the training samples may include at least two sampleimage sequences and at least one gold standard image sequence. That is,a relationship between the count of the at least one corrected imagesequence output by the motion correction model and the count of the atleast two image sequences input to the motion correction model may bethe same as a relationship between a count of the at least one goldstandard image sequence and a count of the at least two sample imagesequences. For example, the count of the at least one corrected imagesequence output by the motion correction model may be the same as thecount of the at least one gold standard image sequence included in eachtraining sample used for training the motion correction model. The countof the at least two image sequences may be the same as the count of theat least two sample image sequences included in each training sampleused for training the motion correction model. More descriptionsregarding the training samples may be found elsewhere in the presentdisclosure (e.g., FIG. 7 and the description thereof).

In some embodiments, the at least one corrected image sequence maycorrespond to a specific time period (also referred to as a second timeperiod). For example, the at least one corrected image sequence mayinclude M corrected image sequences. M may be an integer equal to orgreater than 1. The M corrected image sequences may correspond to Msecond time periods. The duration of a second time period may be thesame as a duration of a first time period (that corresponds to a rankedimage sequence input into the motion correction model). Alternatively,the duration of the second time period may be different from theduration of the first time period. In some embodiments, as the durationof a second time period is relatively short, the at least one correctedimage sequence corresponding to the second time period may berepresented as at least one corrected image sequence corresponding to aspecific time point relating to the second time period (e.g., a starttime point, an end time point, or a middle time point of the second timeperiod).

In some embodiments, the at least one second time period correspondingto the at least one corrected image sequence may be determined dependingon the training samples used for training the motion correction model.The at least two sample image sequences included in each training samplemay correspond to at least two third time periods. The at least one goldstandard image sequence included in each training sample may correspondto at least one fourth time period. That is, a relationship between theat least one second time period and the at least two first time periodsmay be the same as a relationship between the at least one fourth timeperiod corresponding and the at least two third time periods. Forexample, a count of the at least one second time period may be the sameas a count of the at least one fourth time period. The count of the atleast two first time periods may be the same as a count of the at leasttwo third time periods. As another example, a relative position of theat least one second time period with respect to the at least two firsttime periods may be the same as a relative position of the at least onefourth time period with respect to the at least two third time period.For illustration purposes, the following description is provided withreference to at least two image sequences within a motion cycle. Forexample, the count (or number) of the at least two image sequences maybe 2. The two image sequences may correspond to two first time periodsdenoted by 0 s-0.05 s and 0.05 s-0.1 s. The motion correction model maybe trained using the training samples each including two sample imagesequences and a gold standard image sequence. The gold standard imagesequence may correspond to a fourth time period in the middle of twothird time periods corresponding to the two sample image sequences and aduration of the fourth time period may be the same as a duration of athird time period. Accordingly, the at least one corrected imagesequence determined by inputting the two image sequences to the motioncorrection model may include only one corrected image sequence. The onlyone corrected image sequence may correspond to a second time period inthe middle of the two first time periods and the duration of the secondtime period may be the same as the duration of a first time period,e.g., the second time period being denoted by 0.025 s-0.075 s.Alternatively, the motion correction model may be trained using thetraining samples each including two sample image sequences and two goldstandard image sequences. Each of the two gold standard image sequencesmay correspond to one of the two sample image sequences. Accordingly,the at least one corrected image sequence determining by inputting thetwo image sequences to the motion correction model may include twocorrected image sequences each of which corresponds to one of the twoimage sequences. The two corrected image sequences may correspond to twosecond time periods corresponding to the two first time periods, e.g.,the two second time periods being denoted by 0 s-0.05 s and 0.05 s-0.1s, respectively.

As another example, the count (or number) of the at least two imagesequences may be equal to 3, and the three image sequences maycorrespond to three first time periods denoted by 0 s-0.05 s, 0.05 s-0.1s, and 0.1 s-0.15 s. The motion correction model may be trained usingtraining samples each including three sample image sequences and a goldstandard image sequence. The three sample image sequence may correspondto three third time periods. The gold standard image sequence maycorrespond to a fourth time period in the middle of the three timeperiods and a duration of the fourth time period may be the same as aduration of each of the three third time periods. The at least onecorrected image sequence determined by inputting the three imagesequences into the motion correction model may include only onecorrected image sequence. The only one corrected image sequence maycorrespond to a second time period in the middle of the three first timeperiods, e.g., the second time period being denoted by 0.05 s-0.1 s.

As still another example, the count (or number) of the at least twoimage sequences may be equal to 4, and the four image sequences maycorrespond to four first time periods denoted by 0 s-0.05 s, 0.05 s-0.1s, 0.1 s-0.15 s, and 0.15 s-0.2 s. The motion correction model may betrained using training samples each including four sample imagesequences and two gold standard image sequences. The four sample imagesequences may correspond to four third time periods. The two goldstandard image sequences may correspond to two fourth time periods eachof which within the middle of the three time periods and a duration of afourth time period may be the same as a duration of a third time period.The at least one corrected image sequence determined by inputting thefour image sequences to the motion correction model may include twocorrected image sequences. The two corrected image sequences maycorrespond to two second time periods each of which is within the fourfirst time periods, e.g., the two second time periods being denoted by0.05 s-0.1 and 0.1 s-0.15 s, respectively. Alternatively, the motioncorrection model may be trained using training samples each includingfour sample image sequences and a gold standard image sequence. The foursample image sequences may correspond to four third time periods. Thegold standard image sequence may correspond to a fourth time period inthe middle of the three time periods and a duration of the fourth timeperiod may be the same as a duration of a third time period. The atleast one corrected image sequence determined by inputting the fourimage sequences to the motion correction model may include only onecorrected image sequence corresponding to a second time period in themiddle of the four first time periods, e.g., the second time periodbeing denoted by 0.075 s-0.125 s.

It should be noted that the above description regarding the process 500is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, one or more operations of the process500 may be omitted and/or one or more additional operations may beadded. For example, a storing operation may be added elsewhere in theprocess 500. In the storing operation, the processing device 120A maystore information and/or data (e.g., the at least one corrected imagesequence) used or obtained in other operations of the process 500 in astorage device (e.g., the storage device 130) disclosed elsewhere in thepresent disclosure. As another example, an additional operation fortransmitting the at least one corrected image to a terminal device fordisplay may be added after operation 503. In some embodiments, duringthe application of the motion correction model, the processing device120A may input both the at least two image sequences and the at leasttwo time periods to the motion correction model. The processing device120A may output the at least one corrected image sequence by the motioncorrection model.

FIG. 6 is a schematic diagram illustrating an exemplary process ofdetermining at least two image sequences according to some embodimentsof the present disclosure. The processing device 120A may obtain aplurality image data sets relating to a scan of the heart of a patient(not shown). The plurality of image data sets may be acquired by amedical imaging device (e.g., the medical imaging device 110) during oneof a plurality of time periods. The processing device 120A may alsoobtain an ECG image 610 relating to the heart of the patient. The ECGimage 610 may be acquired by a physiological motion detection device(e.g., an ECG monitor device) during the plurality of time periods.According to the ECG image 610, a duration of the scan including theplurality of time periods may be denoted by 611. The heart of thepatient may undergo a cardiac motion including three complete cardiacmotion cycles (denoted by 613, 615, and 617, respectively). Theprocessing device 120A may determine three time periods (denoted by 601,603, and 605, respectively) from the plurality of time periods, which issimilar to the determination of at least two first time periods asdescribed elsewhere in the present disclosure (e.g., operation 501 andthe description thereof). The processing device 120A may determine threeimage data sets from the plurality of image data sets based on the threetime periods. Each of the three image data sets may be acquired duringone of the three time periods. The processing device 120A may determinethree image sequences corresponding to the three time periods (denotedby 601, 603, and 605, respectively) by reconstructing the three imagedata sets. Further, the processing device 120A may determine a correctedimage sequence (not shown) based on the three image sequences by using amotion correction model as described elsewhere in the presentdisclosure.

FIG. 7 is a flowchart illustrating an exemplary process for generating amotion correction model according to some embodiments of the presentdisclosure. In some embodiments, process 700 may be implemented as a setof instructions (e.g., an application) stored in a storage device (e.g.,the storage device 130, storage 220, and/or storage 390). The processingdevice 120B (e.g., the processor 210, the CPU 340, and/or one or moremodules illustrated in FIG. 4B) may execute the set of instructions, andwhen executing the instructions, the processing device 120B may beconfigured to perform the process 700. The operations of the illustratedprocess presented below are intended to be illustrative. In someembodiments, the process 700 may be accomplished with one or moreadditional operations not described and/or without one or more of theoperations discussed. Additionally, the order of the operations ofprocess 700 illustrated in FIG. 7 and described below is not intended tobe limiting. In some embodiments, the motion correction model describedin operation 503 may be obtained according to the process 700. In someembodiments, the process 700 may be performed by another device orsystem other than the imaging system 100, e.g., a device or system of avendor of a manufacturer. For illustration purposes, the implementationof the process 700 by the processing device 120B is described as anexample.

In 701, the processing device 120B (e.g., the obtaining module 405) mayobtain a plurality of training samples. Each of the plurality oftraining samples may include at least two sample image sequencesrelating to a sample subject and at least one gold standard imagesequence corresponding to the at least two sample image sequences.

As used herein, a sample subject refers to an object whose data is usedfor training the motion correction model. The sample subjects may meet acertain preset condition. For example, the sample subjects may be of asame gender and/or of similar ages. As another example, the samplesubjects may be of the same type of organ, tissue, etc. For instance,the sample subjects may be the hearts of different patients. In someembodiments, the sample subjects may include the subject to be scannedas described in connection with FIG. 5, the description of which is notrepeated here. For example, data from the subject to be scanned obtainedin one or more prior scans may be used as the training data for trainingthe motion correction mode. That is, a training sample may include atleast two prior image sequences of the subject acquired when a priorscan is performed on the subject and at least one gold standard imagesequences relating to the subject. The sample subjects may includeobjects other than the subject to be scanned, e.g., organs fromdifferent patients.

The at least two sample image sequences may correspond to at least twosample time periods (also referred to as third time periods). A sampletime period may be similar to the first time period as described in FIG.5, the description of which is not repeated here. For example, theduration of the sample time period may be shorter than a duration of amotion cycle of the sample subject. The duration of the sample timeperiod may range from 0 s-3 s (e.g., 0.05 s, 0.1 s, 0.2 s, 0.3 s, etc.)for a cardiac motion cycle being 1 s. In some embodiments, as theduration of the third time period is relatively short, the third timeperiod may be represented by a time point relating to the third timeperiod, which is similar to the first time period.

As used herein, a sample image sequence relating to the sample subjectrefers to an image sequence of the sample subject that is reconstructedbased on image data of the sample subject acquired by a medical imagingdevice (e.g., the medical imaging device 110) during one of the at leasttwo sample time periods, which is similar to an image sequence of thesubject as described in connection with operation 501. The samplesubject may undergo a physiological motion during the at least twosample time periods. In some embodiments, the at least two sample imagesequences may be stored in a storage device (e.g., the storage device130 of the imaging system 100, or an external resource such as a medicalinstitution (e.g., a disease examination center, a hospital, etc.) or anopen-source database). The open-source database may include a Githubdatabase, an ISBI database, an LIDC-IDRI database, a DDSM MIAS database,a Cancer Imaging Archive database, an OsiriX database, a NITRC database,etc.). The processing device 120B may obtain the at least two sampleimage sequences from the storage device. Alternatively, the processingdevice 120B may obtain at least two sample image data sets relating tothe sample subject. The processing device 120B may determine the atleast two sample image sequences by reconstructing the at least twosample image data sets using a reconstruction algorithm as describedelsewhere in the present disclosure.

The at least one gold standard image may correspond to at least onefourth time period. As used herein, a fourth time period may be similarto the second time period as described in FIG. 5, the description ofwhich is not repeated here. For example, the duration of the fourth timeperiod may be the same as or different from the duration of the thirdtime period. As another example, the at least one fourth time period maycorrespond to at least one of the at least two third time periods. Insome embodiments, as the duration of the fourth time period isrelatively short, the fourth time period may be represented by a timepoint relating to the fourth time period, which is similar to the secondtime period. As used herein, a gold standard image sequence refers to amotion artifact-free image sequence (also referred to as a ground truthimage sequence or a labeled image sequence) of the sample subject.Alternatively, the gold standard image sequence may be regarded ashaving no motion artifact for brevity. For example, the motionartifact-free image sequence may have no detectable artifact caused bythe physiological motion according to a standard (e.g., determined by acomputing device according to an artifact detection algorithm or afeature recognition algorithm or by an observer). As another example,the motion artifact-free image sequence may have an artifact that isless than a threshold artifact. As another example, the motionartifact-free image sequence may be a corrected image sequence that isdetermined using a motion correction algorithm. For instance, theprocessing device 120B may segment the at least two sample imagesequences relating to the sample subject. The processing device 120B maydetermine a motion trajectory of the sample subject by registering thesegmented sample image sequences. The processing device 120B may correctthe motion artifact based on the determined motion trajectory togenerate the at least one gold standard image sequence. In someembodiments, the processing device 120B may obtain the at least one goldstandard image sequence from the storage device where the at least twoimage sequences are obtained.

In some embodiments, a count (or number) of the at least two sampleimage sequences of each of at least some of the plurality of trainingsamples may be equal to a count (or number) of the at least two imagesequences of the subject to be used as an input of a motion correctionmodel trained using the training samples. For example, if the motioncorrection model is trained using the plurality of training samples eachof which includes five sample image sequences, in an application of themotion correction model, 5 image sequences need to be input to themotion correction model. Similarly, a count (or number) of the at leastone gold standard image sequences may be equal to the count (or number)of the at least one corrected image sequences of the subject the motioncorrection model outputs in a specific application. For example, if themotion correction model is trained using the plurality of trainingsamples each of which includes a gold standard image sequence, themotion correction model in a specific application may include onecorrected image sequence.

In 703, the processing device 120B (e.g., the training module 407) maygenerate the motion correction model by training a machine learningmodel using the plurality of samples.

In some embodiments, the machine learning model may include a deeplearning model. The deep learning model may include a neural networkmodel, such as a U-NET model (e.g., a residual U-NET model, a denseU-NET model), a V-NET model, a super-resolution convolutional neuralnetwork (SRCNN) model, etc.

In some embodiments, the processing device 120B may divide the pluralityof training samples into a first portion and a second portion (e.g.,randomly). The first portion may be used to train the machine learningmodel to obtain the motion correction model. The second portion may beused to test the motion correction model to determine whether the motioncorrection model is satisfactory. In some embodiments, a ratio of acount (or number) of the first portion and a count (or number) of thesecond portion may be 8:2, 9:1, 9.5:0.5, etc.

In some embodiments, the machine learning model may include one or moremodel parameters. The processing device 120B may initialize parametervalue(s) of the model parameter(s) before training, and one or more ofthe value(s) of the model parameter(s) may be updated during thetraining of the machine learning model. Exemplary model parameters ofthe preliminary model may include the number (or count) of layers, thenumber (or count) of kernels, a kernel size, a stride, a padding of eachconvolutional layer, a loss function, or the like, or any combinationthereof.

For illustration purposes, the following description is provided withreference to each training sample including at least two training imagesequences and only one gold standard image sequence. In someembodiments, the training of the machine learning model may include oneor more iterations. Taking a current iteration of the one or moreiterations as an example, the processing device 120B may input at leasttwo sample image sequences of the first portion to an updated machinelearning model which is obtained in a previous iteration. The processingdevice 120B may output a corrected sample image sequence by the updatedmachine learning model. Further, the processing device 120B maydetermine an assessment result that indicates the accuracy and/orefficiency of the updated machine learning model.

In some embodiments, the assessment result may be associated with adifference between the corrected sample image sequence and a goldstandard image sequence corresponding to the at least two sample imagesequences of the first portion. For example, the processing device 120Bmay determine a loss function to measure the difference. In someembodiments, the assessment result may be associated with the number (orcount) of iterations that have been performed. Additionally oralternatively, the assessment result may be associated with the number(or count) of training samples that have been used to train the updatedmachine learning model. In some embodiments, the assessment result mayinclude a determination of whether a termination condition is satisfiedin the current iteration. For example, the termination condition may bedeemed satisfied if a value of the loss function is minimal or smallerthan a threshold (e.g., a constant). As another example, the terminationcondition may be deemed satisfied if the value of the loss functionconverges. In some embodiments, convergence may be deemed to haveoccurred if, for example, the variation of values of loss functions intwo or more consecutive iterations is equal to or smaller than athreshold (e.g., a constant). As still another example, the terminationcondition may be deemed satisfied if a certain count of iterations thathave been performed. As a further example, the termination condition maybe deemed satisfied if a certain count of the training samples that havebeen used.

In some embodiments, in response to determining that the terminationcondition is satisfied in the current iteration, the processing device120B may designate the updated machine learning model as a trainedmachine learning model. In other words, parameters of the updatedmachine learning model may be designated as parameters of the trainedmachine learning model. In response to determining that the terminationcondition is not satisfied, the processing device 120B may update, basedon the assessment result, parameter value(s) of the updated machinelearning model to be used in a next iteration. Merely by way of example,the processing device 120B may update the parameter value(s) of theupdated machine learning model based on the value of the loss functionaccording to, for example, a Backpropagation through time (BPTT)algorithm. In some embodiments, the updated machine learning model mayinclude a plurality of parameter values, and updating parameter value(s)of the updated machine learning model refers to updating at least aportion of the parameter values of the updated machine learning model.

After the trained machine learning model is determined, the processingdevice 120B may input at least two sample image sequences of each of thesecond portion into the trained machine learning model. The processingdevice 120B may output a corrected sample image sequence by the trainedmachine learning model for the at least two sample image sequences. Theprocessing device 120B may determine whether the corrected sample imagesequence is consistent with a gold standard image sequence correspondingto the at least two sample image sequences of the second portion todetermine a corresponding testing result. For example, the processingdevice 120B may determine a similarity value between the correctedsample image sequence and the corresponding gold standard imagesequence. The processing device 120B may determine whether thesimilarity value exceeds a similarity threshold. In response todetermining that the similarity value exceeds a similarity threshold,the processing device 120B may determine that the corrected sample imagesequence is consistent with the corresponding gold standard imagesequence, which indicates the corresponding testing result is positive.Further, the processing device 120B may determine whether an accuracyrate of the trained machine learning model is satisfactory. For example,the processing device 120B may determine a ratio of a count (or number)of positive testing results to the count (or number) of the secondportion as the accuracy rate of the trained machine learning model. Theprocessing device 120B may determine whether the accuracy rate isgreater than a threshold (e.g., 85%, 90%, 95%, 98%, etc.). In responseto a determination that the accuracy rate is greater than the threshold,the processing device 120B may designate the trained machine learningmodel as the motion correction model. In response to a determinationthat the accuracy rate is less than the threshold, the processing device120B may initiate new training iterations (e.g., by selecting anothermachine learning model and/or using new training samples, etc.).

In some embodiments, the motion correction model may include a trainedmachine learning model configured to correct motion artifact for imagesequences relating to multiple tissues and/or organs (e.g., the heartand the lung). Alternatively, the motion correction model may include atrained machine learning model configured to correct motion artifact forimage sequences relating to a specific tissue/organ. For example, themotion correction model may include a motion correction model for theheart, a motion correction model for the lung, etc.

It should be noted that the above description regarding process 600 ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, one or more operations may be added oromitted. For example, the motion correction model may be stored in astorage device (e.g., the storage device 130) disclosed elsewhere in thepresent disclosure for further use (e.g., in the determination of the atleast one corrected image sequence as described in connection with FIG.5). In some embodiments, the processing device 120B may divide theplurality of training samples into the first portion, the secondportion, and a third portion. The third portion may be used to verifythe trained machine learning model (e.g., to verify hyper-parameters(e.g., a learning rate) of the trained machine learning model). Then,the second portion may be used to test the verified machine learningmodel. In some embodiments, the processing device 120B may update themotion correction model periodically or aperiodically based on one ormore newly-generated training samples. For example, the processingdevice 120B may update the motion correction model based on a feedbackof the motion correction model when the user uses the motion correctionmodel.

EXAMPLES

The following examples are provided for illustration purposes and notintended to be limiting.

FIG. 8 includes image sequences obtained by image reconstruction ofimage data acquired using a CT device, and exemplary corrected imagesequences. Four image sequences of the heart of a patient acquired usinga CT device are denoted by 802, 804, 806, and 808. The four imagesequences correspond to four time periods denoted by 69%, 72%, 75%, and78% of the cardiac motion cycle of the patient. Assuming a duration of acardiac motion cycle being 1 s and a start time point of the four timeperiods coinciding with a start position of the cardiac motion cycle,69% denotes 0.69 s into the cardiac motion cycle, and the image sequence802 corresponds to a time period denoted by 0.675 s-0.705 s. The fourimage sequences were input into a motion correction model as describedelsewhere in the present disclosure. A corrected image sequence denotedby 810 was output by the motion correction model. A second correctedimage sequence denoted by 820 was obtained using a traditional motioncorrection algorithm based on a motion trajectory of the heart. As shownin FIG. 8, by comparing the corrected image sequence 810 and the secondcorrected image sequence 820, especially, portion A of the correctedimage sequence 810 and portion B of the second corrected image sequence820, the corrected image sequence 810 indicates a better correctioneffect than the second corrected image sequence 820.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer-readable media having computer-readableprogram code embodied thereon.

A non-transitory computer-readable signal medium may include apropagated data signal with computer readable program code embodiedtherein, for example, in baseband or as part of a carrier wave. Such apropagated signal may take any of a variety of forms, includingelectromagnetic, optical, or the like, or any suitable combinationthereof. A computer-readable signal medium may be any computer-readablemedium that is not a computer-readable storage medium and that maycommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.Program code embodied on a computer-readable signal medium may betransmitted using any appropriate medium, including wireless, wireline,optical fiber cable, RF, or the like, or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object-oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C #, VB.NET, Python or the like, conventional procedural programming languages,such as the “C” programming language, Visual Basic, Fortran, Perl,COBOL, PHP, ABAP, dynamic programming languages such as Python, Ruby,and Groovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations, therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose and that the appended claimsare not limited to the disclosed embodiments, but, on the contrary, areintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the disclosed embodiments. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as asoftware-only solution, e.g., an installation on an existing server ormobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereofto streamline the disclosure aiding in the understanding of one or moreof the various inventive embodiments. This method of disclosure,however, is not to be interpreted as reflecting an intention that theclaimed object matter requires more features than are expressly recitedin each claim. Rather, inventive embodiments lie in less than allfeatures of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities, properties, andso forth, used to describe and claim certain embodiments of theapplication are to be understood as being modified in some instances bythe term “about,” “approximate,” or “substantially.” For example,“about,” “approximate” or “substantially” may indicate ±20% variation ofthe value it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting effect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

What is claimed is:
 1. A system for motion correction in medicalimaging, comprising: at least one storage device including a set ofinstructions; and at least one processor configured to communicate withthe at least one storage device, wherein when executing the set ofinstructions, the at least one processor is configured to direct thesystem to perform operations including: obtaining at least two imagesequences relating to a subject, wherein each of the at least two imagesequences is reconstructed based on image data that is acquired by amedical imaging device during one of at least two time periods and thesubject undergoes a physiological motion during the at least two timeperiods; and generating, based on the at least two image sequences, atleast one corrected image sequence relating to the subject bycorrecting, using a motion correction model, an artifact caused by thephysiological motion.
 2. The system of claim 1, wherein each of at leasttwo image sequences relates to the heart of the subject, and thephysiological motion includes a cardiac motion.
 3. The system of claim1, wherein the physiological motion includes a motion cycle.
 4. Thesystem of claim 3, wherein a duration of each of the at least two timeperiods is shorter than a duration of the motion cycle.
 5. The system ofclaim 3, wherein the at least two image sequences are acquired within asame motion cycle or different motion cycles.
 6. The system of claim 1,wherein the obtaining at least two image sequences relating to a subjectincludes: obtaining a plurality of image sequences relating to thesubject; and determining the at least two image sequences from theplurality of image sequences, wherein the at least two image sequencessatisfy a condition relating to a motion amplitude.
 7. The system ofclaim 1, wherein the generating, based on the at least two imagesequences, at least one corrected image sequence relating to the subjectusing a motion correction model includes: determining at least tworanked image sequences by ranking, based on the at least two timeperiods, the at least two image sequences; inputting, according to theranking of the at least two image sequences, the at least two rankedimage sequences into the motion correction model; and outputting the atleast one corrected image sequence by the motion correction model. 8.The system of claim 7, wherein the at least two image sequences areacquired within a same motion cycle, and the determining at least tworanked image sequences by ranking, based on the at least two timeperiods, the at least two sample image sequences includes: ranking,according to a chronological order of the at least two time periods, theat least two sample image sequences.
 9. The system of claim 7, whereinthe at least two image sequences are acquired within different motioncycles, and the determining at least two ranked image sequences byranking, based on the at least two sample time periods, the at least twosample image sequences includes: for each of the at least two timeperiods, determining a relative position of the time period with respectto its corresponding motion cycle; ranking, according to an order of theat least two relative positions, the at least two time periods; andranking, according to the at least two ranked time periods, the at leasttwo sample image sequences.
 10. The system of claim 1, wherein themotion correction model is obtained according to operations including:obtaining a plurality of samples each of which includes at least twosample image sequences relating to a sample subject and at least onegold standard image sequence corresponding to the at least two sampleimage sequences, wherein each of the at least two image sequences isreconstructed based on image data acquired by the medical imaging deviceduring one of at least two sample time periods and the at least one goldstandard image sequence has no motion artifact; and generating themotion correction model by training a machine learning model using theplurality of samples.
 11. A method for motion correction in medicalimaging, the method being implemented on a computing device including atleast one processor and at least one storage device, the methodcomprising: obtaining at least two image sequences relating to asubject, wherein each of the at least two image sequences isreconstructed based on image data that is acquired by a medical imagingdevice during one of at least two time periods and the subject undergoesa physiological motion during the at least two time periods; andgenerating, based on the at least two image sequences, at least onecorrected image sequence relating to the subject by correcting, using amotion correction model, an artifact caused by the physiological motion.12. The method of claim 11, wherein the generating, based on the atleast two image sequences, at least one corrected image sequencerelating to the subject using a motion correction model includes:determining at least two ranked image sequences by ranking, based on theat least two time periods, the at least two image sequences; inputting,according to the ranking of the at least two image sequences, the atleast two ranked image sequences into the motion correction model; andoutputting the at least one corrected image sequence by the motioncorrection model.
 13. The method of claim 12, wherein the at least twoimage sequences are acquired within a same motion cycle, and thedetermining at least two ranked image sequences by ranking, based on theat least two time periods, the at least two sample image sequencesincludes: ranking, according to a chronological order of the at leasttwo time periods, the at least two sample image sequences.
 14. Themethod of claim 13, wherein the at least two image sequences areacquired within different motion cycles, and the determining at leasttwo ranked image sequences by ranking, based on the at least two sampletime periods, the at least two sample image sequences includes: for eachof the at least two time periods, determining a relative position of thetime period with respect to its corresponding motion cycle; ranking,according to an order of the at least two relative positions, the atleast two time periods; and ranking, according to the at least tworanked time periods, the at least two sample image sequences.
 15. Themethod of claim 11, wherein the motion correction model is obtainedaccording to operations including: obtaining a plurality of samples eachof which includes at least two sample image sequences relating to asample subject and at least one gold standard image sequencecorresponding to the at least two sample image sequences, wherein eachof the at least two image sequences is reconstructed based on image dataacquired by the medical imaging device during one of at least two sampletime periods and the at least one gold standard image sequence has nomotion artifact; and generating the motion correction model by traininga machine learning model using the plurality of samples.
 16. A systemfor generating a motion correction model, comprising: at least onestorage device including a set of instructions; and at least oneprocessor configured to communicate with the at least one storagedevice, wherein when executing the set of instructions, the at least oneprocessor is configured to direct the system to perform operationsincluding: obtaining a plurality of samples each of which includes atleast two sample image sequences relating to a sample subject and atleast one gold standard image sequence corresponding to the at least twosample image sequences, wherein each of the at least two image sequencesis reconstructed based on image data acquired by a medical imagingdevice during one of at least two sample time periods, the samplesubject undergoes a physiological motion during the at least two sampletime periods, and the at least one gold standard image sequence has noartifact caused by the physiological motion; and generating the motioncorrection model by training a machine learning model using theplurality of samples.
 17. The system of claim 16, wherein each of atleast two sample image sequences relates to the heart of a samplesubject, and the physiological motion includes a cardiac motion.
 18. Thesystem of claim 16, wherein the physiological motion includes a motioncycle.
 19. The system of claim 18, wherein a duration of each of the atleast two sample time periods is shorter than a duration of the motioncycle.
 20. The system of claim 4, wherein the generating the motioncorrection model by training a machine learning model using theplurality of samples includes: for each of the plurality of samples,ranking, according to the at least two sample time periods, the at leasttwo sample image sequences; and generating the motion correction modelby training the machine learning model using the at least two rankedimage sequences and the at least one gold standard image sequencecorresponding to each of the plurality of samples.